The role of loneliness and negative schemas in the moment-to-moment dynamics between social anxiety and paranoia

Social anxiety and paranoia often co-occur and exacerbate each other. While loneliness and negative schemas contribute to the development of social anxiety and paranoia separately, their role in the development of the two symptoms co-occurring is rarely considered longitudinally. This study examined the moment-to-moment relationship between social anxiety and paranoia, as well as the effects of loneliness and negative schemas on both experiences individually and coincidingly. A total of 134 non-clinical young adults completed experience sampling assessments of momentary social anxiety, paranoia, and loneliness ten times per day for six consecutive days. Participants’ negative-self and -other schemas were assessed with the Brief Core Schema Scale. Dynamic structural equation modelling revealed a bidirectional relationship between social anxiety and paranoia across moments. Loneliness preceded increases in both symptoms in the next moment. Higher negative-self schema was associated with a stronger link from paranoia to social anxiety; whereas higher negative-other schema was associated with a stronger link from social anxiety to paranoia. Our findings support the reciprocal relationship between social anxiety and paranoia. While loneliness contributes to the development of social anxiety and paranoia, negative self and other schemas appear to modify the relationships between the two symptoms.

The between-person correlations between negative schemas and DSEM parameters for Model 2 are reported in Table 3.The level of negative-self was positively associated with the strength of the cross-lagged effect from paranoia to social anxiety (r = 0.32, 95% CrI [0.11, 0.51]).The level of negative-other schema was positively associated with the strength of the cross-lagged effect from social anxiety to paranoia (r = 0.30, 95% CrI [0.11, 0.49]), but negatively associated with the strength of the cross-lagged effect from loneliness to paranoia (r = − 0.23, 95% CrI [− 0.43, − 0.02]).Both levels of negative-self (rs = 0.35-0.45)and -other schemas (rs = 0.23-0.29)were associated with mean social anxiety, paranoia and loneliness.

Discussion
This study utilized ESM to repeatedly assess momentary symptoms in daily life and found that social anxiety predicted an increase in paranoia across moments and vice versa.Such reciprocal relationships were demonstrated in a sample of young adults in the absence of full-blown psychiatric disorders.These relationships did not differ between genders.Our findings showed that social anxiety and paranoia do not merely co-exist, but also dynamically interact with one another in their development and maintenance.
In addition to previous longitudinal studies which considered social anxiety and paranoia separately 9,19,23 , our analytical approach using DSEM focused on the covariation of both symptoms in a single model (Model 1).For the first time, we offered evidence for the bidirectional relationship within the same sample, revealing comparable effect sizes of each directional path.In addition to previous conceptualization of social anxiety as an antecedent to paranoia (e.g.cognitive model of paranoia, Freeman et al. 18 ), our results also supported it as a consequence of paranoia as shown in other studies 9,23 .Future studies may clarify the overlap of paranoid thinking with the affective, cognitive and behavioral manifestations of social anxiety, which would inform the underlying processes in both symptoms.
We then took a closer look at loneliness in the moment-to-moment dynamics between social anxiety and paranoia (Model 2).We found that loneliness predicted an increase in both social anxiety and paranoia, corroborating with a longitudinal study with a community sample 27 .We confirmed the 'healthy' status of our sample with a psychiatric interview; therefore, our findings reflected the relationship between social anxiety and paranoia free from the confounding effects by treatments and chronicity of the psychiatric disorders.The convergent finding from Lim et al. 27 and our study support loneliness as a common psychopathological pathway to both social anxiety and paranoia.While previous studies have found that loneliness leads to heightened vigilance for social threat via a myriad of affective and social-cognitive processes 29,30 , the contributions of these processes in differentiating social anxiety from paranoia outcomes need to be ascertained in further studies.
As hypothesized, the level of negative-self schema was associated with a stronger relationship from paranoia to social anxiety, whereas the level of negative-other schema was associated with a stronger relationship from www.nature.com/scientificreports/social anxiety to paranoia.These findings were in line with the proposed role of negative beliefs about self (e.g.'I am worthless and weak') in the formation of fear of rejection and criticism implicated in social anxiety 31,32 .The findings also supported the specificity between negative-other schema and paranoia 10,35,36 , where negative beliefs about others (e.g.'Others are harsh and bad') would exacerbate the formation of paranoid thinking, possibly against the backdrop of social anxiety.Importantly, our findings highlighted the presence of both negative-self and -other schemas to be necessary to the maintenance of the reciprocal relationship between social anxiety and paranoia.This speculation is consistent with the finding of a recent latent profile analysis by Chau et al. 10 .They identified a subgroup of non-clinical young adults high on both social anxiety and paranoia, who reported more negative-self and -other schemas than subgroups high on either symptom.Future studies may examine how various constellations of negative-self-and -other schemas would shape the development of various phenotypic expressions of social anxiety and paranoia.Our findings also pave ways for future investigation of the potential between-person heterogeneity in these moment-to-moment dynamics, which may longitudinally predict the transition into social anxiety disorder, schizophrenia and their co-morbidity.
In sum, our findings offered support to Aunjitsakul et al. 37 's unified framework for the understanding the psychopathological processes underlying social anxiety and paranoia.In particular, loneliness appears to be a situational trigger to the emergence of social anxiety and paranoia, in which their dynamics are strengthened by negative schemas.Our findings further extended Aunjitsakul et al. 's 37 cognitive model with the role of negative-other schemas, which may exaggerate the appraisal of social threat in terms of harm and malevolence, which define paranoia 35 .Our findings shed light on the possibility of ameliorating social anxiety and paranoia via interventions that reduce loneliness 45,46 or challenge negative-self and -other schemas (e.g.cognitive restructuring [47][48][49] ).
There are several limitations of the current study.First, our results may be specific to the current sampling frequency of ESM assessment.Despite the statistical adjustment to confine the temporal effects to one-hour windows, it is inevitable that any effects that operate at shorter or longer time windows would be missed.Second, our data collection was conducted during the COVID-19 pandemic, a period when exacerbated loneliness, social anxiety and paranoia were reported [50][51][52] .Although the baseline levels of these phenomena were comparable to another sample of demographically diverse non-clinical young adults tested before the outbreak of the pandemic 10 (N = 2089), we could not ascertain the confounding impact of the pandemic on the expression of these phenomena in daily life 36 .Third, a majority of our sample were undergraduate students.It is not sure whether our results would be replicated in demographically diverse samples.Finally, we acknowledge the possibility that the dynamics between social anxiety and paranoia may also involve other unmeasured mechanisms beyond loneliness and negative core schemas, such as interpersonal trauma 53,54 .This should be investigated in future research.
Using ESM, the current findings supported the reciprocal relationship between social anxiety and paranoia.Loneliness was also found to predict increases in both anxiety and paranoia across moments, suggesting that loneliness predates and may lead to the increases in both symptoms.Moreover, the strength of the dynamics between social anxiety and paranoia was associated with levels of negative-self and -other schemas.Our findings shed new light on the understanding of the dynamics between social anxiety and paranoia, which may invite replications in the clinical populations.

Methods
Ethics approval for the study was granted by the Survey and Behavioral Research Ethics Committee of The Chinese University of Hong Kong (Reference no.: SBRE-19-788).All methods were carried out in accordance with relevant guidelines and regulations.Informed consent was obtained from all participants.

Participants
Eligible participants aged 18-30 were recruited either from the subject pool of the Introductory Psychology course or via campus recruitment.Participants with any past or current psychiatric diagnosis (self-reported and then confirmed with a diagnostic clinical interview, see Measures) and who could not read Chinese were excluded.We targeted a sample size of 130, which is comparable to previous ESM studies with non-clinical samples analyzed using the dynamics structural equational modelling (DSEM) (see Statistical Analysis) [55][56][57] .Our targeted sample size fulfilled the sample size recommendation from a recent simulation study for DSEM 58 .

Procedure
Data collection took place in June to October 2021.It happened to be after the peak of the fourth wave of the COVID-19 pandemic in Hong Kong.While face-to-face data collection was allowed by the university, territorywide infection control measures such as social distancing and mask-wearing mandate were in place.Consented participants attended a 1-h assessment session during which they were screened with the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-IV (SCI-DSM-IV; So et al. 59 ).Participants without any past or current psychiatric diagnosis completed a baseline survey, and were then briefed individually on the ESM procedure.
The ESM questionnaires were programmed into a smartphone app (SEMA3 60 ) installed on the participant's mobile phone.Adopting a signal-contingent sampling design, the app prompted participants to answer the same set of items assessing momentary loneliness, social anxiety and paranoia (see Measures below) ten times a day for six consecutive days.The app displayed the items one by one in a way that the preceding item had to be answered before the next item would appear.The prompt signals were pseudo-randomized into blocks of time intervals within 13 waking hours.The starting time of the ESM assessment was tailored for each participant to maximize compliance.Consecutive ESM questionnaires were set at least 15  Support was rendered to the participants by the research team throughout the ESM assessment period.On the first assessment day, a research worker contacted the participant to ensure that the app was functioning properly and to encourage them to answer to the ESM prompts.In the middle of the week, the research worker monitored the participant's progress and offered help to increase their compliance when necessary.Participants could also contact the research team whenever they encountered any difficulties with the app.After completing the 6-day ESM assessment, participants received course credits or monetary compensation for their time.

Measures
Baseline survey Participants completed retrospective questionnaires assessing levels of loneliness, paranoia, depression, and social anxiety.These included the UCLA-Loneliness Scale version 3 (UCLA-LS-v3) 61 , the Revised Green et al.Paranoid Thoughts Scale (R-GPTS) 62 , the Patient Health Questionannire-9 (PHQ-9) 63 and the Social Interaction Anxiety Scale-6/Social Phobia Scale-6 (SIAS-6/SPS-6) 64 .The UCLA-LS-v3 and SIAS-6/SPS-6 do not specify the timeframe of reference, whereas PHQ-9 and R-GPTS assess depressive symptoms and paranoia within two weeks and one month respectively.The Chinese versions of these measures have been validated and used in previous studies 10,26,36 .Negative-self and -other schemas were measured with the respective subscales of the Brief Core Schema Scale 65 .Its Chinese version has been used in Chau et al. 10 and So et al. 36 .Internal consistencies of these measures ranged from 0.78 to 0.93 in this sample.Items on age, gender, educational attainment, monthly household income and employment status were also included.

ESM assessment
All ESM measures were rated on a 7-point Likert scale (1 "not at all"-7 "very").
Momentary loneliness.The 3-item UCLA Loneliness scale 66 was modified to assess momentary loneliness (e.g., 'I lack companionship right now').These three items have been used in a previous ESM study 29 .In the current study, the within-and between-person reliabilities were 0.86 and 0.98 respectively.Momentary social anxiety.Momentary social anxiety was assessed with the three items suggested in Kashdan and Steger 67 (e.g., 'I worried that I would say or do something wrong right now').It has been used in previous ESM studies 68,69 .In the current study, the within-and between-person reliabilities were 0.84 and 0.99 respectively.Momentary paranoia.Momentary paranoia was assessed with the five items suggested by Schlier et al. 70 (e.g., 'People are trying to upset me right now').These items have been used in previous ESM studies [70][71][72] .The within-(0.84)and between-person (0.99) reliabilities were good in the current study.

Statistical analysis
In accordance with previous ESM studies, responses from participants who completed less than one-third of the total ESM questionnaires (i.e.20) were excluded from the data analysis 73 .Our hypotheses were tested with Dynamic Structural Equation Modeling (DSEM) 40,41 .DSEM allows the examination of multi-level relationships among ESM variables by decomposing the intensive longitudinal data into within-and between-person variance components using a latent person-mean approach.For the within-person components, the fixed effects of means of ESM variables (i.e.intercepts), their autoregressive effects and cross-lagged effects were simultaneously estimated in a single model.The autoregressive effects were estimated by regressing the variables at the current moment t on the same variables at the previous moment t-1, while the cross-lagged effects were estimated by regressing the variable at the current moment t on another variable at the previous moment t-1.To allow for inter-individual differences in these fixed effects, the DSEM estimated all the random effects at the betweenperson level, which were allowed to correlate with each other.
Bayesian estimation is supported in DSEM to estimate all random effects in a single model with high accuracy and computational efficiency.The default non-informative priors were used in this study.Four Markov Chain Monte Carlo (MCMC) chains with 5000 iterations each were used, with a thinning of 10.Missing data was assumed to be missing at random and handled with MCMC sampling 40 .Within-person standardized parameters of the fixed effects 74 were computed for interpretation.Estimates of all fixed effects were regarded as statistically significant if their 95% credible intervals (CrIs) did not include zero.Tests for model comparison were not conducted, as model comparison is an underdeveloped area for DSEM 40 .
To control for potential trends or non-stationarity of ESM data, the hour of measurement was added in the within-person level of the DSEM models as fixed effects.Unequal time spacing of the ESM data was handled by creating time grids of one hour using a discrete time filter approach using the Mplus option TINTERVAL 40 .Therefore, the interpretation of all parameters was in reference to the time window of one hour.A simulation study indicated that estimates of parameters are unbiased up to 80-85% of missing data 58 .
For the first hypothesis, we fitted the bivariate multilevel first-order vector autoregressive model (Model 1) with within-person cross-lagged effects between momentary social anxiety and paranoia, while controlling for their autoregressive effects (see schematic representation in Fig. 1).For the second hypothesis, we further added momentary loneliness into the model, creating a Model 2 that examined the within-person cross-lagged effects between loneliness, social anxiety and paranoia, while controlling for their autoregressive effects (see Fig. 2).As exploratory analyses, we also examined gender differences in these effects by adding gender as a predictor at the between-person level of these models.The third hypothesis was tested by the correlation between the random effects at the between-person level and the levels of negative-self and -other schemas, which were grand-mean centred before entering into the model.

Figure 1 .
Figure 1.Schematic representation of the DSEM model of social anxiety and paranoia (Model 1).Note: This figure is a schematic representation of the dynamic structural equation model of social anxiety and paranoia (Model 1).The left panel contains the decomposition of social anxiety and paranoia into within-person and between-person variance components respectively.The top right panel indicates the within-person level model, which is a vector autoregressive model.The bottom right panel indicates the between-person level model, which includes all the random effects of the model, corresponding to the solid black circles in the within-person level model.SA-social anxiety, PAR-paranoia.

Figure 2 .
Figure 2. Schematic representation of the DSEM model of loneliness, social anxiety and paranoia (Model 2).Note: This figure is a schematic representation of the dynamic structural equation model of social anxiety, paranoia and loneliness (Model 2).The left panel contains the decomposition of social anxiety, paranoia and loneliness into within-person and between-person variance components respectively.The top right panel indicates the within-person level model, which is a vector autoregressive model.The bottom right panel indicates the between-person level model, which includes the levels of negative-self and -other schemas, as well as all the random effects of the model, corresponding to the solid black circles in the within-person level model.SA-social anxiety, PAR-paranoia, LONE-loneliness, NS-negative-self subscore of the Brief Core Schema Scale, NO-negative-other subscore of the Brief Core Schema Scale.

Table 3 .
Between-person correlations between negative schemas and random effects (Model 2).SA social anxiety, PAR paranoia, LONE loneliness.Significant correlations based on the 95% credible interval are in bold typeface.See the notation of the parameters in Model 2.